---

name: configuration-data description: "Initialize, inspect, and troubleshoot GraphRAG settings, environment interpolation, auth, input readers, storage/cache/table providers, vector stores, and offline data readiness before indexing or querying." disable-model-invocation: true metadata: disco-role: operating license: MIT

GraphRAG Configuration and Data

Use this sub-skill when a task mentions settings.yaml, .env, GraphRagConfig, Azure/OpenAI auth, LiteLLM model settings, input file schemas, input_storage, output_storage, cache, table_provider, vector_store, LanceDB, Azure AI Search, Cosmos DB, or safe checks before running an index.

Route Boundaries

• Stay here for initialization, config loading, schema validation, provider selection, secret-safe summaries, and offline input reader checks.
• Route full index execution, update runs, workflow selection, and interpreting PipelineRunResult outputs to ../indexing/.
• Route completed-index DataFrame loading and search calls to ../querying/.
• Route prompt generation and graphrag prompt-tune usage to ../prompt-tuning/.
• Route custom reader/provider implementation, factory registration, and package-extension design to ../package-extensions/.

Fast Workflow

1. Create or locate a project root containing settings.yaml, settings.yml, or settings.json; graphrag init --root <project> generates the default settings.yaml and .env skeleton.
2. Fill completion_models and embedding_models with LiteLLM-compatible model_provider, model, auth_method, and either api_key or Azure managed identity fields.
3. Set input.type and reader columns, then match input_storage.type and input_storage.base_dir or remote storage settings to the data source.
4. Choose local or remote output_storage, cache.storage, table_provider, and vector_store providers before indexing.
5. Validate without network calls:

   python scripts/inspect_config.py --root <project>
   python scripts/smoke_input_readers.py
   

6. If config and readers pass, continue with indexing in ../indexing/; do not run indexing just to validate settings.

Key APIs and Commands

• from graphrag.config.load_config import load_config loads settings.[yaml|yml|json] from a root and returns GraphRagConfig.
• load_config(root_dir, cli_overrides={...}) recursively overlays CLI-style nested dictionaries before Pydantic validation.
• GraphRagConfig validates file base directories, resolves file paths, expands LanceDB db_uri, and overlays default vector schemas for GraphRAG embeddings.
• graphrag init --root <project> writes default config content with ${GRAPHRAG_API_KEY} interpolation and common prompt file paths.
• graphrag index --root <project> belongs to ../indexing/ after this sub-skill has checked prerequisites.

Provider Matrix

• Inputs: text, csv, json, jsonl, markitdown, and parquet through graphrag_input.create_input_reader.
• Storage: file, memory, blob, and cosmosdb via StorageConfig, depending on the section using it.
• Cache: json, memory, and none; JSON cache uses a nested storage block.
• Tables: parquet, csv, and cosmosdb through table_provider.type.
• Vectors: lancedb, azure_ai_search, and cosmosdb through vector_store.type.

References

• See references/config-schema-and-env.md for GraphRagConfig, model auth, .env, and validation behavior.
• See references/input-storage-cache-vector.md for reader schemas and storage/cache/table/vector provider settings.
• See references/troubleshooting.md for common validation failures and doc/code drift traps.

Bundled Offline Checks

• scripts/inspect_config.py loads a project config and prints a JSON summary with secret-like values redacted.
• scripts/smoke_input_readers.py creates tiny temporary text, csv, json, jsonl, and optional parquet inputs and reports local reader readiness without credentials or network access.